The use of statistical reconstruction to facilitate iterative reconstruction of an image through a series of consecutive estimations and/or guesses is a known area of prior art endeavor. Such an iterative statistical reconstruction process typically works by characterizing candidate reconstructed image data with respect to an overall likelihood of accuracy (and/or some other metric of choice) to thereby identify a best selection.
Transitions in an image (for example, from one object to another such as, in an x-ray image, from bone to water, or from less-dense bone to more-dense bone) often represent a considerable challenge in these regards. Generally speaking, an iterative statistical reconstruction process works favorably when configured to eschew unrealistic transitions. Furthermore, measurement noise can lead to false transitions appearing in the data, and in general a good statistical reconstruction process should account for the measurement noise, in effect removing or reducing the false transitions. That said, transitions, even abrupt transitions, can and do occur in the real world, and in an ideal imaging system those real transitions should be preserved. Accordingly, accommodating real transitions (especially abrupt transitions) but downgrading a candidate reconstructed image having one or more false transitions is both a goal and a challenge.
Statistical reconstruction also generally requires the use of optimization algorithms. Many approaches to optimization in these regards are known. In many cases the practical applicability of a given approach in a given application setting is limited by the computational intensity dictated by that setting and approach. While many approaches are ultimately capable of providing an acceptable result, from a practical standpoint in a real-life application setting many approaches either take too much time to converge, require too much in the way of computational resources to physically implement for a reasonable cost, are unstable or unpredictable, and/or are inflexible with the types of statistical models that they can accommodate.
Elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions and/or relative positioning of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments of the present teachings. Also, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments of the present teachings. Certain actions and/or steps may be described or depicted in a particular order of occurrence while those skilled in the art will understand that such specificity with respect to sequence is not actually required. The terms and expressions used herein have the ordinary technical meaning as is accorded to such terms and expressions by persons skilled in the technical field as set forth above except where different specific meanings have otherwise been set forth herein.